{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chained Visualizations with Yellowbrick Pipelines\n",
    "\n",
    "In Yellowbrick, `VisualPipelines` are modeled on Scikit-Learn `Pipelines`, which allow us to chain estimators together in a sane way and use them as a single estimator. This is very useful for models that require a series of extraction, normalization, and transformation steps in advance of prediction. For more about Scikit-Learn Pipelines, check out [this post](http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html) by Zac Stewart.\n",
    "\n",
    "`VisualPipelines` sequentially apply a list of transforms, visualizers, and a final estimator which may be evaluated by additional visualizers. Intermediate steps of the pipeline must be kinds of 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`.\n",
    "\n",
    "Any step that implements draw or show methods can be called sequentially directly from the VisualPipeline, allowing multiple visual diagnostics to be generated, displayed, and saved on demand. If `draw` or `show` is not called, the visual pipeline should be equivalent to the simple pipeline to ensure no reduction in performance.\n",
    "\n",
    "The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. These steps can be visually diagnosed by visualizers at every point in the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"/Users/rebeccabilbro/Desktop/waves/stuff/yellowbrick\")\n",
    "\n",
    "import requests\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import yellowbrick as yb \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## The path to the test data sets\n",
    "FIXTURES  = os.path.join(os.getcwd(), \"data\")\n",
    "\n",
    "## Dataset loading mechanisms\n",
    "datasets = {\n",
    "    \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n",
    "    \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n",
    "    \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n",
    "    \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n",
    "}\n",
    "\n",
    "\n",
    "def load_data(name, download=False):\n",
    "    \"\"\"\n",
    "    Loads and wrangles the passed in dataset by name.\n",
    "    If download is specified, this method will download any missing files. \n",
    "    \"\"\"\n",
    "    \n",
    "    # Get the path from the datasets \n",
    "    path = datasets[name]\n",
    "    \n",
    "    # Check if the data exists, otherwise download or raise \n",
    "    if not os.path.exists(path):\n",
    "        if download:\n",
    "            download_all() \n",
    "        else:\n",
    "            raise ValueError((\n",
    "                \"'{}' dataset has not been downloaded, \"\n",
    "                \"use the download.py module to fetch datasets\"\n",
    "            ).format(name))\n",
    "        \n",
    "    \n",
    "    # Return the data frame\n",
    "    return pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the classification data set\n",
    "data = load_data('credit') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Specify the features of interest\n",
    "features = [\n",
    "        'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n",
    "        'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n",
    "        'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n",
    "        'jul_pay', 'aug_pay', 'sep_pay',\n",
    "    ]\n",
    "\n",
    "classes = ['default', 'paid']\n",
    "\n",
    "# Extract the numpy arrays from the data frame \n",
    "X = data[features].as_matrix()\n",
    "y = data.default.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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HH37AhAkTcPr0aRQWFsLPzw9FRUUIDAyUXAfniRARyUAlY3D67LPPcPXqVYwePRqzZ8/G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\ngdCoSE0fbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf16470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features.rankd import Rank2D \n",
    "\n",
    "visualizer = Rank2D(features=features, algorithm='covariance')\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u7oSGhiKTyfD09KSsrAwHBwdmzJiBi4sL165dQ6/XA+VLtUy8vb3x9vaudMyz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\nN6VmDbYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf16240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.features.radviz import RadViz\n",
    "\n",
    "visualizer = RadViz(classes=classes, features=features)\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ok now try with `VisualPipeline`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yellowbrick.pipeline import VisualPipeline\n",
    "\n",
    "from yellowbrick.features.rankd import Rank2D \n",
    "from yellowbrick.features.radviz import RadViz\n",
    "\n",
    "\n",
    "multivisualizer = VisualPipeline([\n",
    "    ('rank2d', Rank2D(features=features, algorithm='covariance')), \n",
    "    ('radviz', RadViz(classes=classes, features=features)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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agaJ5SSEXQgijUvR70bpRNC81r0oIIYQoJ6RHLoQQBqXqw1t6UTUvKeRCCGFU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\nn2fc5shh7HzywYMHGRgYQKfToaoqUVH/+R8RDoc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x103e24908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multivisualizer.fit(X, y)\n",
    "multivisualizer.transform(X)\n",
    "multivisualizer.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
